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KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning

Haotian Si, Changhua Pei, Xiao He, Zeyan Li, Zhe Xie, Zexin Wang, Jiyao Hu, Zhaoyang Yu, Tieying Zhang, Dan Pei, Jianhui Li, Gaogang Xie

TL;DR

This work proposes a two-round reinforcement learning framework that boosts performance but also preserves intrinsic reasoning ability and significantly improves generalization to unseen scenarios and provides a practical framework for real-world time series intelligence, which is in urgent demand.

Abstract

Driven by the increasingly complex and decision-oriented demands of time series analysis, we introduce the Semantic-Conditional Time Series Reasoning task, which extends conventional time series analysis beyond purely numerical modeling to incorporate contextual and semantic understanding. To further enhance the mode's reasoning capabilities on complex time series problems, we propose a two-round reinforcement learning framework: the first round strengthens the mode's perception of fundamental temporal primitives, while the second focuses on semantic-conditioned reasoning. The resulting model, KairosVL, achieves competitive performance across both synthetic and real-world tasks. Extensive experiments and ablation studies demonstrate that our framework not only boosts performance but also preserves intrinsic reasoning ability and significantly improves generalization to unseen scenarios. To summarize, our work highlights the potential of combining semantic reasoning with temporal modeling and provides a practical framework for real-world time series intelligence, which is in urgent demand.

KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning

TL;DR

This work proposes a two-round reinforcement learning framework that boosts performance but also preserves intrinsic reasoning ability and significantly improves generalization to unseen scenarios and provides a practical framework for real-world time series intelligence, which is in urgent demand.

Abstract

Driven by the increasingly complex and decision-oriented demands of time series analysis, we introduce the Semantic-Conditional Time Series Reasoning task, which extends conventional time series analysis beyond purely numerical modeling to incorporate contextual and semantic understanding. To further enhance the mode's reasoning capabilities on complex time series problems, we propose a two-round reinforcement learning framework: the first round strengthens the mode's perception of fundamental temporal primitives, while the second focuses on semantic-conditioned reasoning. The resulting model, KairosVL, achieves competitive performance across both synthetic and real-world tasks. Extensive experiments and ablation studies demonstrate that our framework not only boosts performance but also preserves intrinsic reasoning ability and significantly improves generalization to unseen scenarios. To summarize, our work highlights the potential of combining semantic reasoning with temporal modeling and provides a practical framework for real-world time series intelligence, which is in urgent demand.
Paper Structure (44 sections, 5 equations, 18 figures, 4 tables)

This paper contains 44 sections, 5 equations, 18 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Preliminaries and Related Works
  3. Problem Definition
  4. Time Series Reasoning Models
  5. KairosDataPipe & KairosVL
  6. Taxonomy
  7. Fact-Grounded Feature Extraction.
  8. Predictive Tasks.
  9. Event-Aware Feature Filtering.
  10. Counterfactual Inference.
  11. KairosDataPipe
  12. Data Generation Agent.
  13. LLM Judge Groups.
  14. Plot Generation & Manual Check.
  15. KairosVL
  16. ...and 29 more sections

Figures (18)

  • Figure 1: Comparison of time series task formulations from the perspectives of Numerical Data Processing and Semantic-Conditional Reasoning (taking the real-world time series anomaly diagnosis task as an example). While the majority of existing studies remain confined to the former, real-world applications also urge for the latter, an end-to-end reasoning paradigm.
  • Figure 2: Details of KairosDataPipe and KairosDataset.
  • Figure 3: One-round reinforcement learning pipeline v.s. two-round reinforcement learning pipeline.
  • Figure 4: Ablation studies on different training pipeline.
  • Figure 5: Visualization of some metrics in training process.
  • ...and 13 more figures